Spreadsheet Tricks for Thermal Engineers
: Monte Carlo simulationI wrote about this subject in another newsletter, and I've been wanting to use Monte Carlo modeling ever since. I finally
had a chance to do it recently. (Thanks to Garron Morris and CoolingZone for the helpful articles.) Monte Carlo modeling takes random samples of input distributions. You put
together a behavioral model (for example, a thermal network, or a linearization of data from a physical or numerical experiment). The output of the behavioral model is a distribution of the output parameter.
As an example (not the one I did for a client...), consider a "simple" lidded flip chip package with a heat sink on it. The average impinging velocity has some variation from part to part
within the system, and possibly also system to system. The performance of the two thermal interface materials also varies. Using a spreadsheet, I extracted a fast behavioral model for the
thermal resistance of the heat sink in impinging flow (based on a paper
). The maximum die temperature is a function of this thermal resistance, and also of the power (which varies part to part even under constant
load).The input distributions, which in this case are fictitious (note zero means of TIM distributions -- I rolled nominal values into the theta equation), might look something like this:
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